The value of urine biochemical parameters in the prediction of the severity of coronavirus disease 2019

Rui Liu 1 , Qingfeng Mahttp://orcid.org/https://orcid.org/0000-0002-1676-3235 2 , Huan Han 1 , Hanwen Su 1 , Fang Liu 3 , 4 , Kailang Wu 3 , Wei Wang 5  and Chengliang Zhu 1
  • 1 Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, P.R. China
  • 2 Department of Clinical Laboratory, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
  • 3 State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, Hubei, P.R. China
  • 4 Wuhan Institute of Biotechnology, Wuhan, Hubei, P.R. China
  • 5 Department of Clinical Laboratory, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
Rui Liu
  • Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, P.R. China
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, Qingfeng MaORCID iD: https://orcid.org/0000-0002-1676-3235, Huan Han
  • Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, P.R. China
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, Hanwen Su
  • Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, P.R. China
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, Fang Liu
  • State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, Hubei, P.R. China
  • Wuhan Institute of Biotechnology, Wuhan, Hubei, P.R. China
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, Kailang Wu
  • State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, Hubei, P.R. China
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, Wei Wang
  • Department of Clinical Laboratory, Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
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and Chengliang Zhu
  • Corresponding author
  • Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, P.R. China
  • Email
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Abstract

Background

Among patients with coronavirus disease 2019 (COVID-19), the cases of a significant proportion of patients are severe. A viral nucleic acid test is used for the diagnosis of COVID-19, and some hematological indicators have been used in the auxiliary diagnosis and identification of the severity of COVID-19. Regarding body fluid samples, except for being used for nucleic acid testing, the relationship between COVID-19 and routine body fluid parameters is not known. Our aim was to investigate the value of urine biochemical parameters in the prediction of the severity of COVID-19.

Methods

A total of 119 patients with COVID-19 were enrolled at Renmin Hospital of Wuhan University. According to the severity of COVID-19, the patients were divided into three groups (moderate 67, severe 42 and critical 10), and 45 healthy persons were enrolled in the same period as healthy controls. The relationship between the results of urine biochemical parameters and the severity of COVID-19 was analyzed.

Results

The positive rates of urine occult blood (BLOOD) and proteinuria (PRO) were higher in COVID-19 patients than in healthy controls (p < 0.05); the urine specific gravity (SG) value was lower in patients than in healthy controls (p < 0.05), and the urine potential of hydrogen (pH) value was higher in patients than in healthy controls (p < 0.01). The positive rates of urine glucose (GLU-U) and PRO in the severe and critical groups were higher than those in the moderate group (p < 0.01 and p < 0.05, respectively); other biochemical parameters of urine were not associated with the severity of COVID-19.

Conclusions

Some urine biochemical parameters are different between patients with severe acute respiratory syndrome (SARS)-CoV-2 and healthy controls, and GLU-U and PRO may be helpful for the differentiation of COVID-19 severity.

Introduction

Coronavirus is an RNA virus with an enveloped positive-sense RNA that can grow in epithelial cells and mainly cause respiratory infection, including severe acute respiratory syndrome (SARS), and acute exacerbation of chronic bronchitis (AECB) in humans, and the SARS-CoV epidemic caused great social panic in 2003 [1]. A novel coronavirus, designated SARS-CoV-2, was first discovered in 2019 in Wuhan, China [2]. The virus is a novel human pathogen and has the ability to infect multiple host species [3].

The disease caused by SARS-CoV-2 was named coronavirus disease 2019 (COVID-19) by the WHO [4], and this term mainly refers to the ongoing outbreak of SARS-CoV-2-infected pneumonia in China [2], [5], [6]. Patients with severe cases of COVID-19 show dyspnea accompanied by hypoxemia, and some of them have acute respiratory distress syndrome (ARDS), septic shock and multiple organ failure. The abnormal hematology results of patients with COVID-19 include lower lymphocytes, higher lactate dehydrogenase (LDH), higher creatine kinase (CK) and its isoenzymes and higher C-reactive protein (CRP) and inflammatory factors.

Urine dry chemical tests have the characteristics of being quick, convenient and economical, and the biochemical parameters of urine can be used for the auxiliary diagnosis of urinary tract infections (UTIs) [7], [8], [9], the diagnosis of kidney diseases and the monitoring of treatment effects. Until now, there have been no reports about the correlation between urine biochemical parameters and COVID-19; therefore, the aim of this study was to explore the value of urine biochemical parameters in the prediction of the severity of COVID-19.

Materials and methods

Study design and participants

This study was approved by the Institutional Ethics Board of Renmin Hospital of Wuhan University (No. WDRY2020-K066). A total of 119 patients with COVID-19 enrolled in this study were diagnosed with SARS-CoV-2 infection in Renmin Hospital (Wuhan University, China) from January 31 to February 26, 2020 and represented the case group along with 45 healthy controls. The case group was divided into three additional subgroups (Table 1) according to the Diagnosis and Treatment Program of New Coronavirus Pneumonia (sixth trial version). Any patient or control with hypertension, diabetes, UTIs or other diseases were excluded from the analysis. The information of the four groups is listed in Table 1, which shows that there was no significant difference in terms of sex or age among the four groups (p>0.05).

Table 1:

The information of the case group and the control group.

GroupGendernAge (mean±SD)
Healthy controlMale2361.9±10.7
Female22
COVID-19: moderateMale3563.0±11.9
Female32
COVID-19: severeMale2263.9±11.3
Female20
COVID-19: criticalMale562.2±12.7
Female5
χ2=0.032

p>0.05
F=0.519

p>0.05

Method

After patients were admitted to the Renmin Hospital, approximately 20 mL of clean midstream urine samples was obtained from the subjects in each group the next morning. For critical COVID-19 patients, the urine samples were collected from catheter. Biochemical parameters of urine such as urine occult blood (BLOOD), proteinuria (PRO), bilirubin, urobilinogen, potential of hydrogen (pH), specific gravity (SG), ketone (KET), urine glucose (GLU-U), nitrite and leukocytes (LEU) were tested using a fully automatic urine biochemical analyzer (Combi-Scan XL, Shenzhen Baodi Technology Co., China) and supporting kits (Combi-UriScreen 11 SYS PLUS, Shenzhen Baodi Technology Co., China). The urine sediments were tested using an automatic sediment analyzer (Cobio S120, 77 Elektronika Muszeripari Kft, Hungary). All the collected specimens were tested within 2 h. In the case of delay, the samples were refrigerated. For suspicious results, the samples were centrifuged 5 min at 400g, and then the microscopy checks of urinary sediments were performed by professional inspectors simultaneously.

Statistical analysis

SPSS 22.0 statistical software was used for statistical analysis. Count data were analyzed using the chi-square (χ2) test. Normally distributed measurement data were expressed as the mean± SD and were analyzed using t-test between two samples. A p-value <0.05 indicates statistical significance.

Results

The value of urine biochemical parameters in the diagnosis of COVID-19

The positive rates of BLOOD and PRO were significantly higher in patients with COVID-19 than in healthy controls (p<0.05). The differences in SG and pH values between patients with COVID-19 and healthy controls were statistically significant (p<0.05). The detailed results are shown in Table 2.

Table 2:

The comparison of urine biochemical parameters between patients and healthy controls.

nBLOODLEUPROSGpH
COVID-1911949 (41.17%)18 (15.13%)34 (28.57%)1.020±0.0076.27±0.60
Healthy4510 (22.22%)5 (11.11%)5 (11.11%)1.023±0.0075.94±0.70
χ2/t5.0930.4375.4922.0222.907
p-Value0.0240.5090.0190.0450.004

BLOOD, urine occult blood; LEU, leukocytes; PRO, proteinuria; SG, specific gravity; pH, potential of hydrogen.

The value of urine biochemical parameters in differentiating the severity of COVID-19

The differences in the positive rates of GLU-U and PRO among the different severities of COVID-19 were statistically significant (p<0.05). The positive rates of GLU-U and PRO were significantly higher in individuals with severe COVID-19 than in those with moderate COVID-19 (p<0.05). The differences in the detection rates of BLOOD, KET, LEU, red blood cell count (RBC) and white blood cell count (WBC) were not statistically significant among the different severities of COVID-19 (p>005). The detailed results are shown in Table 3.

Table 3:

The evaluation of the severity of COVID-19 through urine biochemical parameters.

Moderate (n=67)Severe (n=42)Critical (n=10)χ2p-Value
BLOOD27 (40.3%)16 (38.1%)6 (60%)1.6490.438
GLU-U16 (23.88%)38 (90.48%)6 (60%)46.220.000
KET8 (11.94%)6 (14.29%)2 (20%)0.5110.774
LEU11 (16.42%)5 (11.9%)2 (20%)0.6120.737
PRO12 (17.91%)17 (40.48%)5 (50%)8.8980.012
RBC52 (77.61%)30 (71.43%)7 (70%)0.6560.720
WBC59 (88.06%)37 (88.1%)7 (70%)2.5710.277

KET, ketone; RBC, red blood cell count; WBC, white blood cell count.

Discussion

The outbreak of the COVID-19 epidemic started in Wuhan, China at the end of 2019, has spread worldwide, and has led to a large number of patient deaths and huge economic losses in China. COVID-19 was identified by the WHO as the sixth public health emergency of international concern. The cause of COVID-19 was determined to be SARS-CoV-2 by an RNA-based metagenomic next-generation sequencing approach [10]. The clinical symptoms of COVID-19 are nonspecific, including diarrhea, nausea, fever, cough and myalgia, or COVID-19 can be relatively asymptomatic [11], [12]. Older men with comorbidities are more likely to have respiratory failure, and the disease onset of some patients has shown rapid progression to multiple organ dysfunction [5]; many patients die from critical COVID-19.

According to the most recently published Diagnosis and Treatment Program of 2019 New Coronavirus Pneumonia (sixth trial version), COVID-19 patients are divided into mild, moderate, severe and critical types [13]. Some hematological parameters (e.g. WBC, lymphopenia, CRP, LDH, CK and troponin) were associated with the severity of COVID-19 [14], [15].

In our study, the positive rates of BLOOD and PRO in COVID-19 patients were found to be higher than those in healthy controls, and the values of SG and pH were also different between COVID-19 patients and healthy controls. However, the positive rate of LEU was not significantly different between COVID-19 patients and healthy controls. The results indicate that the differences in BLOOD, PRO, SG and pH are caused by SARS-CoV-2 infection, not bacterial infection. These four indicators can be used for the auxiliary differentiation of COVID-19 patients from healthy individuals.

Due to the large number of symptomatic patients with COVID-19, the asymptomatic diagnosed patients are rarely treated in hospitals and are instead mainly assigned to temporary isolation points for treatment; therefore, it is difficult to obtain detailed information on asymptomatic patients. Even so, the urine biochemical results were still interesting, and GLU-U and PRO were associated with COVID-19 severity. The positive rates of GLU-U and PRO were significantly different among the different severities of COVID-19. Compared with moderate COVID-19 patients, the positive rates of GLU-U and PRO were significantly increased in the severe and critical groups. However, BLOOD, LEU, RBC and WBC showed no difference among the moderate, severity and critical groups, implying that elevated PRO was not caused by a bacterial infection in the urinary system, but by SARS-CoV-2 infection. ARDS is the main symptom of patients with COVID-19 in China [16], and SARS-CoV-2 infection may trigger cytokine storm which causes multiple organ dysfunction syndrome (MODS) including kidney [17]. Kidney damage caused by cytokine storm should be responsible for the difference of GLU-U and PRO in the three groups of COVID-19 patients. Furthermore, the positive rate of KET was not significantly different among the different severities of COVID-19, and temporary hyperglycemia did not cause the patient’s tendency toward ketoacidosis, which indirectly verified that the patient’s shock was mainly due to impaired lung function.

Under these circumstances, urine biochemical parameters are not only useful for the identification of COVID-19, but are also helpful in the evaluation of the dynamic changes in patients with COVID-19. We hence reinforce the suggestion that routine urine biochemical tests may be useful for the evaluation of COVID-19 progression.

Research funding: This work was supported by the National Mega Project on Major Infectious Disease Prevention under Grant 2017ZX10103005, the National Natural Science Foundation of China (81672079) (Funder Id: http://dx.doi.org/10.13039/501100001809).

Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

Competing interests: Authors state no conflict of interest.

Informed consent: Informed consent was obtained from all individuals included in this study.

Ethical approval: This study was approved by the Institutional Ethics Board of Renmin Hospital of Wuhan University (No. WDRY2020-K066).

References

  • 1.

    Bian Y, Zhou J, Liao M. Research progress of coronavirus non-structural proteins. Chin J Anim Infect Dis 2013;21:67–74.

  • 2.

    Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382:727–33.

  • 3.

    Fung TS, Liu DX. Human coronavirus: host-pathogen interaction. Annu Rev Microbiol 2019;73:529–57.

  • 4.

    World Health Organization. WHO Director-General’s remarks at the media briefing on 2019-nCoV on 11 February 2020. https://www.who.int/dg/speeches/detail. Published February 11, 2020.

  • 5.

    Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497–506.

  • 6.

    Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199–207.

  • 7.

    Berger RE. The urine dipstick test useful to rule out infections. A meta-analysis of the accuracy. J Urol 2005;174:941–2.

  • 8.

    Falbo R, Sala MR, Signorelli S, Venturi N, Signorini S, Brambilla P. Bacteriuria screening by automated whole-field image-based microscopy reduces the number of urine cultures. J Clin Microbiol 2012;50:1427–9.

  • 9.

    Erdman P, Anderson B, Zacko JC, Taylo K, Donaldson K. The accuracy of the Sysmex UF-1000i in urine bacterial detection compared with the standard urine analysis and culture. Arch Pathol Lab Med 2017;141:1540–3.

  • 10.

    Chen L, Liu W, Zhang Q, Xu K, Ye G, Wu W, et al. RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak. Emerg Microbes Infect 2020;9:313–9.

  • 11.

    Guan WJ, Ni ZY, Hu Y, Liang WH, Qu CQ, He JX. et al. Clinical characteristics of 2019 novel coronavirus infection in China. N Engl J Med 2020; DOI: 10.1056/NEJMoa2002032. [Epub ahead of print].

  • 12.

    Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 2020;395:514–23.

  • 13.

    General Office of National Health Committee. Office of State Administration of Traditional Chinese Medicine. Notice on the issuance of a program for the diagnosis and treatment of novel coronavirus (2019-nCoV) infected pneumonia (trial sixth edition) (2020-02-19). http://yzs.satcm.gov.cn/zhengcewenjian/2020-02-19/13221.html.

  • 14.

    Chen N, Zhou M, Dong X, Dong X, Qu J, Gong F. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–13.

  • 15.

    Wang D, Hu B, Hu C, Zhu J, Liu X, Zhang J. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. J Am Med Assoc 2020;323:1061–9.

  • 16.

    Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. J Am Med Assoc 2020; DOI:. [Epub ahead of print].

    • Crossref
    • Export Citation
  • 17.

    Tetro JA. Is COVID-19 receiving ADE from other coronaviruses? Microbes Infect 2020;22:72–3.

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  • 1.

    Bian Y, Zhou J, Liao M. Research progress of coronavirus non-structural proteins. Chin J Anim Infect Dis 2013;21:67–74.

  • 2.

    Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382:727–33.

  • 3.

    Fung TS, Liu DX. Human coronavirus: host-pathogen interaction. Annu Rev Microbiol 2019;73:529–57.

  • 4.

    World Health Organization. WHO Director-General’s remarks at the media briefing on 2019-nCoV on 11 February 2020. https://www.who.int/dg/speeches/detail. Published February 11, 2020.

  • 5.

    Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497–506.

  • 6.

    Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199–207.

  • 7.

    Berger RE. The urine dipstick test useful to rule out infections. A meta-analysis of the accuracy. J Urol 2005;174:941–2.

  • 8.

    Falbo R, Sala MR, Signorelli S, Venturi N, Signorini S, Brambilla P. Bacteriuria screening by automated whole-field image-based microscopy reduces the number of urine cultures. J Clin Microbiol 2012;50:1427–9.

  • 9.

    Erdman P, Anderson B, Zacko JC, Taylo K, Donaldson K. The accuracy of the Sysmex UF-1000i in urine bacterial detection compared with the standard urine analysis and culture. Arch Pathol Lab Med 2017;141:1540–3.

  • 10.

    Chen L, Liu W, Zhang Q, Xu K, Ye G, Wu W, et al. RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak. Emerg Microbes Infect 2020;9:313–9.

  • 11.

    Guan WJ, Ni ZY, Hu Y, Liang WH, Qu CQ, He JX. et al. Clinical characteristics of 2019 novel coronavirus infection in China. N Engl J Med 2020; DOI: 10.1056/NEJMoa2002032. [Epub ahead of print].

  • 12.

    Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 2020;395:514–23.

  • 13.

    General Office of National Health Committee. Office of State Administration of Traditional Chinese Medicine. Notice on the issuance of a program for the diagnosis and treatment of novel coronavirus (2019-nCoV) infected pneumonia (trial sixth edition) (2020-02-19). http://yzs.satcm.gov.cn/zhengcewenjian/2020-02-19/13221.html.

  • 14.

    Chen N, Zhou M, Dong X, Dong X, Qu J, Gong F. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–13.

  • 15.

    Wang D, Hu B, Hu C, Zhu J, Liu X, Zhang J. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. J Am Med Assoc 2020;323:1061–9.

  • 16.

    Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. J Am Med Assoc 2020; DOI:. [Epub ahead of print].

    • Crossref
    • Export Citation
  • 17.

    Tetro JA. Is COVID-19 receiving ADE from other coronaviruses? Microbes Infect 2020;22:72–3.

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